BUSTER: a "BUSiness Transaction Entity Recognition" dataset
This provides a domain-specific dataset to support industry-oriented research in finance, addressing gaps like noisy data and long documents, but it is incremental as it focuses on data creation rather than novel methods.
The authors tackled the challenge of applying NLP to real-world business cases by creating BUSTER, a manually annotated dataset of 3779 documents for financial transaction entity recognition, and established baselines with the best model used to automatically annotate an additional 6196 documents as a silver corpus.
Albeit Natural Language Processing has seen major breakthroughs in the last few years, transferring such advances into real-world business cases can be challenging. One of the reasons resides in the displacement between popular benchmarks and actual data. Lack of supervision, unbalanced classes, noisy data and long documents often affect real problems in vertical domains such as finance, law and health. To support industry-oriented research, we present BUSTER, a BUSiness Transaction Entity Recognition dataset. The dataset consists of 3779 manually annotated documents on financial transactions. We establish several baselines exploiting both general-purpose and domain-specific language models. The best performing model is also used to automatically annotate 6196 documents, which we release as an additional silver corpus to BUSTER.